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llive 完全解説 (0) — series index: 大分類 8 記事 + 全体図

コンセプト hook: llive (FullSense ™ の思考層) を構成する技術 / アルゴリズム
を名称ごとに解説する series の入口です. 1 記事に詰め込むと ~80k 字級になるため,
大分類 8 記事 に分割しました. 本 index は全体地図 — どの章で何を読めるかを示します.

0. この series について

llive は「LLM 本体ではなく LLM の周りに被せる認知 OS」です. その内部を 4 層
(認知 / 最適化 / 実行 / 横断) × 8 章に分けて, 各章で具体的な class / function /
機能名まで降りて解説します. 各記事は次の共通構造を持ちます:

冒頭 hook (8 秒で「これは何か」)
具体的な class / function 名まで降りた小節
実コードへの GitHub link
References (学術 / OSS / 内部)
cross-link (前 / 次 / 本 index / repo)

合計 ~80k 字. ja Qiita + en Medium を並走します.

1. Series 構成 (8 大分類)

#	タイトル (クリックで各章)	中分類	公開
01	memory layer — 4 層メモリ	semantic / episodic / structural / parameter / surprise gating	🟢 公開
02	思考因子 + COG-MESH — 10 因子と 9 component	構造化 / 再構成 / 閉ループ / ... / proactive / quarantine / 5W1H	🟡 限定共有
03	構造進化 (TRIZ × Z3)	TRIZ 40 原理 / ChangeOp / verifier / 9 画法	🟡 限定共有
04	収束型最適化 (B-0〜B-9)	SynapticSelector / UCB1 / Hebbian / 本番 hot path	🟡 限定共有
05	進化型最適化 (v0.B/C/D/E)	Genome / Crossover / Tournament / Mutation / lineage	🟡 限定共有
06	LLM backend 層 — non-transformer	Mamba / Jamba / RWKV / Diffusion / 思考因子→SSM Δ Bridge	🟡 限定共有
07	観測 + 統治	runtime_metadata / Approval Bus / governance / honest disclosure	🟡 限定共有
08	lleval (eval framework)	progressive size matrix / 5+1 軸 / judge rotation	🟡 限定共有

🟢 公開 = Qiita ホーム / 検索結果に露出. 🟡 限定共有 = URL を知る人のみ閲覧. 公開昇格は連載順 (01 → 02 → … → 08) で順次予定.

2. 全体図 (8 layer の関係)

「認知層 → 最適化層 → 実行層」の縦が llive の処理 flow,
「観測 + 統治」「lleval」が横断層として全 layer に効く構造です.

3. 想定読者

エンジニア (Python + LLM 基礎知識あり)
AI researcher (LLM の周辺アーキテクチャに興味)
個人 OSS author (実装パターンの参考)
企業 R&D (on-prem LLM stack の検討材料)

4. 公開順 (週 2 本ペース)

週	公開記事
Week 1	01 memory + 02 思考因子
Week 2	03 構造進化 + 04 収束型
Week 3	05 進化型 + 06 LLM backend
Week 4	07 観測統治 + 08 lleval

各記事の en 版は Medium に並走します.

5. 連載を貫くテーマ — 「速い」は実装方法で桁が変わる

連載中核 #24-05 で扱う派生集団進化の hot path 3 つを Rust 化した実測:

RUST-15 persona_dissimilarity_pairwise: avg x12.71 (batch)
RUST-16 collusion_score_kernel: avg x66.70 (numpy 小 N hot path)
RUST-17b novelty_score_batch (rayon + quickselect): avg x9.32

「Rust 化 = 速い」は嘘 / 「numpy = 速い」も嘘 — 実装方法 (FFI 境界 / batch /
numpy zero-copy / 並列度 / partial sort) で結果が桁違いになります. この honest
disclosure の姿勢が連載全体の通奏低音です. 5 パターン判定表は #24-04 / #24-05 /
#24-07 で詳述します.

6. References (本 index)

furuse-kazufumi/llive — 本体 repo
FullSense Spec v1.1 (llive docs/)
各章の References は個別記事に記載

Series Navigation

→ 次: llive 完全解説 (1) 「忘れない LLM」
repo: furuse-kazufumi/llive

English

llive Complete Guide (0) — series index: 8 main chapters + overall map

Concept hook: This is the entrance to a series that explains the
technologies / algorithms that make up llive (the thinking layer of FullSense ™)
by name. Cramming it into one article reaches ~80k characters, so we split it into
8 main chapters. This index is the overall map — it shows what you can read in
which chapter.

0. About this series

llive is "a cognitive OS wrapped around the LLM, not the LLM itself". We divide its
interior into 4 layers (cognition / optimization / execution / cross-cutting) × 8
chapters, and each chapter goes down to concrete class / function / feature names.
Each article has the following common structure:

an opening hook ("what is this" in 8 seconds)
subsections that descend to concrete class / function names
GitHub links to the real code
References (academic / OSS / internal)
cross-links (prev / next / this index / repo)

A total of ~80k characters. We run ja Qiita + en Medium in parallel.

1. Series structure (8 main chapters)

#	Title (click for each chapter)	Subtopics	Visibility
01	memory layer — 4-layer memory	semantic / episodic / structural / parameter / surprise gating	🟢 public
02	thought factors + COG-MESH — 10 factors and 9 components	structurize / recompose / closed-loop / ... / proactive / quarantine / 5W1H	🟡 limited share
03	structural evolution (TRIZ × Z3)	TRIZ 40 principles / ChangeOp / verifier / 9-windows	🟡 limited share
04	convergent optimization (B-0..B-9)	SynapticSelector / UCB1 / Hebbian / production hot path	🟡 limited share
05	evolutionary optimization (v0.B/C/D/E)	Genome / Crossover / Tournament / Mutation / lineage	🟡 limited share
06	LLM backend layer — non-transformer	Mamba / Jamba / RWKV / Diffusion / thought-factor→SSM Δ Bridge	🟡 limited share
07	observability + governance	runtime_metadata / Approval Bus / governance / honest disclosure	🟡 limited share
08	lleval (eval framework)	progressive size matrix / 5+1 axes / judge rotation	🟡 limited share

🟢 public = exposed on the Qiita home / search results. 🟡 limited share = viewable only by those who know the URL. Promotion to public is planned in series order (01 → 02 → … → 08).

2. Overall map (8-layer relationships)

The vertical "cognition → optimization → execution" is llive's processing flow;
"observability + governance" and "lleval" are the cross-cutting layers that
touch every level.

3. Intended readers

engineers (with Python + basic LLM knowledge)
AI researchers (interested in LLM-surrounding architecture)
individual OSS authors (reference for implementation patterns)
corporate R&D (material for considering an on-prem LLM stack)

4. Publishing order (2 articles / week)

Week	Published articles
Week 1	01 memory + 02 thought factors
Week 2	03 structural evolution + 04 convergent
Week 3	05 evolutionary + 06 LLM backend
Week 4	07 observability+governance + 08 lleval

Each article's English version runs in parallel on Medium.

5. The theme running through the series — "fast" changes by orders of magnitude with implementation

Measured results of Rust-porting 3 hot paths of the derived-population evolution
covered in the series centerpiece #24-05:

RUST-15 persona_dissimilarity_pairwise: avg x12.71 (batch)
RUST-16 collusion_score_kernel: avg x66.70 (numpy small-N hot path)
RUST-17b novelty_score_batch (rayon + quickselect): avg x9.32

"Rust = fast" is a lie / "numpy = fast" is also a lie — the result differs by
orders of magnitude depending on the implementation method (FFI boundary / batch /
numpy zero-copy / parallelism / partial sort). This honest-disclosure stance is the
basso continuo of the whole series. The 5-pattern decision table is detailed in
#24-04 / #24-05 / #24-07.

6. References (this index)

furuse-kazufumi/llive — the main repo
FullSense Spec v1.1 (llive docs/)
Each chapter's References are in its own article

Series Navigation

→ Next: llive Complete Guide (1) "The LLM that Never Forgets"
repo: furuse-kazufumi/llive

中文

llive 完全解说 (0) — series index: 8 大分类文章 + 总图

概念 hook: 这是按名称解读 构成 llive (FullSense ™ 思考层) 的技术 / 算法 的
系列的入口. 塞进一篇会达到约 8 万字, 所以分为 8 大分类文章. 本 index 是总图 —
标明在哪一章能读到什么.

0. 关于本系列

llive 是「不是 LLM 本体, 而是包裹在 LLM 外侧的认知 OS」. 我们把它的内部分为
4 层 (认知 / 优化 / 执行 / 贯穿) × 8 章, 每一章都下沉到具体的 class / function /
功能名来讲解. 每篇文章都有以下通用结构:

开头 hook (8 秒说清「这是什么」)
下沉到具体 class / function 名的小节
指向实代码的 GitHub link
References (学术 / OSS / 内部)
cross-link (前 / 次 / 本 index / repo)

总计 约 8 万字. ja Qiita + en Medium 并行.

1. 系列结构 (8 大分类)

#	标题 (点击进入各章)	中分类	可见性
01	memory layer — 4 层记忆	semantic / episodic / structural / parameter / surprise gating	🟢 公开
02	思考因子 + COG-MESH — 10 因子与 9 component	结构化 / 重组 / 闭环 / ... / proactive / quarantine / 5W1H	🟡 限定共享
03	结构进化 (TRIZ × Z3)	TRIZ 40 原理 / ChangeOp / verifier / 9 画法	🟡 限定共享
04	收敛型优化 (B-0..B-9)	SynapticSelector / UCB1 / Hebbian / 生产 hot path	🟡 限定共享
05	进化型优化 (v0.B/C/D/E)	Genome / Crossover / Tournament / Mutation / lineage	🟡 限定共享
06	LLM backend 层 — non-transformer	Mamba / Jamba / RWKV / Diffusion / 思考因子→SSM Δ Bridge	🟡 限定共享
07	观测 + 治理	runtime_metadata / Approval Bus / governance / honest disclosure	🟡 限定共享
08	lleval (eval framework)	progressive size matrix / 5+1 轴 / judge rotation	🟡 限定共享

🟢 公开 = 在 Qiita 首页 / 搜索结果露出. 🟡 限定共享 = 仅知道 URL 的人可看. 公开升级按连载顺序 (01 → 02 → … → 08) 依次进行.

2. 总图 (8 层关系)

纵向「认知层 → 优化层 → 执行层」是 llive 的处理流程,「观测 + 治理」「lleval」
作为贯穿层对所有 layer 起作用.

3. 目标读者

工程师 (有 Python + LLM 基础知识)
AI researcher (对 LLM 周边架构感兴趣)
个人 OSS 作者 (实现模式的参考)
企业 R&D (本地 LLM stack 的考察素材)

4. 发布顺序 (每周 2 篇)

周	发布文章
Week 1	01 memory + 02 思考因子
Week 2	03 结构进化 + 04 收敛型
Week 3	05 进化型 + 06 LLM backend
Week 4	07 观测治理 + 08 lleval

每篇文章的英文版在 Medium 并行.

5. 贯穿系列的主题 —「快」会因实现方法而差好几个数量级

将系列核心 #24-05 处理的派生群体进化的 3 个 hot path 用 Rust 重写的实测:

RUST-15 persona_dissimilarity_pairwise: avg x12.71 (batch)
RUST-16 collusion_score_kernel: avg x66.70 (numpy 小 N hot path)
RUST-17b novelty_score_batch (rayon + quickselect): avg x9.32

「Rust 化 = 快」是谎言 /「numpy = 快」也是谎言 — 结果会因实现方法 (FFI 边界 /
batch / numpy zero-copy / 并行度 / partial sort) 而差好几个数量级. 这种 honest
disclosure 的姿态是整个系列的通奏低音. 5 模式判定表在 #24-04 / #24-05 / #24-07
详述.

6. References (本 index)

furuse-kazufumi/llive — 本体 repo
FullSense Spec v1.1 (llive docs/)
各章的 References 在各自文章中

Series Navigation

→ 下一篇: llive 完全解说 (1) 「不会遗忘的 LLM」
repo: furuse-kazufumi/llive

한국어

llive 완전 해설 (0) — series index: 8 대분류 글 + 전체도

콘셉트 hook: llive (FullSense ™의 사고층)를 구성하는 기술 / 알고리즘을
명칭별로 해설하는 series의 입구다. 한 글에 담으면 약 8만 자가 되므로 8 대분류
글로 나눴다. 본 index는 전체 지도 — 어느 장에서 무엇을 읽을 수 있는지를 나타낸다.

0. 이 series에 대하여

llive는 「LLM 본체가 아니라 LLM 주위에 씌우는 인지 OS」다. 그 내부를
4층 (인지 / 최적화 / 실행 / 횡단) × 8장으로 나누어, 각 장에서 구체적인 class /
function / 기능명까지 내려가 해설한다. 각 글은 다음 공통 구조를 가진다:

서두 hook (8초로 「이것은 무엇인가」)
구체적인 class / function 명까지 내려간 소절
실제 코드로의 GitHub link
References (학술 / OSS / 내부)
cross-link (이전 / 다음 / 본 index / repo)

합계 약 8만 자. ja Qiita + en Medium을 병행한다.

1. Series 구성 (8 대분류)

#	제목 (클릭으로 각 장)	중분류	가시성
01	memory layer — 4층 메모리	semantic / episodic / structural / parameter / surprise gating	🟢 공개
02	사고 인자 + COG-MESH — 10 인자와 9 component	구조화 / 재구성 / 폐루프 / ... / proactive / quarantine / 5W1H	🟡 한정 공유
03	구조 진화 (TRIZ × Z3)	TRIZ 40 원리 / ChangeOp / verifier / 9 화법	🟡 한정 공유
04	수렴형 최적화 (B-0..B-9)	SynapticSelector / UCB1 / Hebbian / 프로덕션 hot path	🟡 한정 공유
05	진화형 최적화 (v0.B/C/D/E)	Genome / Crossover / Tournament / Mutation / lineage	🟡 한정 공유
06	LLM backend 층 — non-transformer	Mamba / Jamba / RWKV / Diffusion / 사고 인자→SSM Δ Bridge	🟡 한정 공유
07	관측 + 통치	runtime_metadata / Approval Bus / governance / honest disclosure	🟡 한정 공유
08	lleval (eval framework)	progressive size matrix / 5+1 축 / judge rotation	🟡 한정 공유

🟢 공개 = Qiita 홈 / 검색 결과에 노출. 🟡 한정 공유 = URL을 아는 사람만 열람. 공개 승격은 연재 순서 (01 → 02 → … → 08)로 순차 예정.

2. 전체도 (8 layer의 관계)

세로 「인지층 → 최적화층 → 실행층」이 llive의 처리 flow,「관측 + 통치」「lleval」
이 횡단층으로서 모든 layer에 작용하는 구조다.

3. 상정 독자

엔지니어 (Python + LLM 기초 지식 보유)
AI researcher (LLM 주변 아키텍처에 관심)
개인 OSS author (구현 패턴의 참고)
기업 R&D (on-prem LLM stack 검토 자료)

4. 공개 순서 (주 2편 페이스)

주	공개 글
Week 1	01 memory + 02 사고 인자
Week 2	03 구조 진화 + 04 수렴형
Week 3	05 진화형 + 06 LLM backend
Week 4	07 관측통치 + 08 lleval

각 글의 en 버전은 Medium에 병행한다.

5. 연재를 관통하는 테마 — 「빠름」은 구현 방법으로 자릿수가 바뀐다

연재 중핵 #24-05에서 다루는 파생 집단 진화의 hot path 3개를 Rust화한 실측:

RUST-15 persona_dissimilarity_pairwise: avg x12.71 (batch)
RUST-16 collusion_score_kernel: avg x66.70 (numpy 작은 N hot path)
RUST-17b novelty_score_batch (rayon + quickselect): avg x9.32

「Rust화 = 빠름」은 거짓 /「numpy = 빠름」도 거짓 — 결과는 구현 방법 (FFI 경계 /
batch / numpy zero-copy / 병렬도 / partial sort)에 따라 자릿수가 달라진다. 이
honest disclosure의 자세가 연재 전체의 통주저음이다. 5 패턴 판정표는 #24-04 /
#24-05 / #24-07에서 상술한다.

6. References (본 index)

furuse-kazufumi/llive — 본체 repo
FullSense Spec v1.1 (llive docs/)
각 장의 References는 각자 글에 기재

Series Navigation

→ 다음: llive 완전 해설 (1) 「잊지 않는 LLM」
repo: furuse-kazufumi/llive